Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem

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dc.contributor.authorPicek, Lukáš
dc.contributor.authorŠulc, Milan
dc.contributor.authorMatas, Jiří
dc.contributor.authorHeilmann-Clausen, Jacob
dc.date.accessioned2023-02-13T11:00:21Z
dc.date.available2023-02-13T11:00:21Z
dc.date.issued2022
dc.description.abstract-translatedThe main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results. © 2022 Copyright for this paper by its authors.en
dc.format12 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationPICEK, L. ŠULC, M. MATAS, J. HEILMANN-CLAUSEN, J. Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem. In Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR-WS, 2022. s. 1970-1981. ISBN: neuvedeno , ISSN: 1613-0073cs
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.obd43937114
dc.identifier.uri2-s2.0-85136986038
dc.identifier.urihttp://hdl.handle.net/11025/51466
dc.language.isoenen
dc.project.IDSS05010008/Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového viděnícs
dc.project.IDSGS-2022-017/Inteligentní metody strojového vnímání a porozumění 5cs
dc.publisherCEUR-WSen
dc.relation.ispartofseriesProceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forumen
dc.rights© authorsen
dc.rights.accessopenAccessen
dc.subject.translatedclassificationen
dc.subject.translatedcomputer visionen
dc.subject.translatedfine grained visual categorizationen
dc.subject.translatedfungien
dc.subject.translatedFungiCLEFen
dc.subject.translatedLifeCLEFen
dc.subject.translatedmachine learningen
dc.subject.translatedmetadataen
dc.subject.translatedopen-set recognitionen
dc.subject.translatedspecies identificationen
dc.titleOverview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problemen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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